States and Their Environemntal Impact
by Alison McGuire, Alyssa Holzer and MacKenzie Olson
Table of Contents
Introduction
How sustainable is your state? Investigating the environmental impacts of the states in America.
Literature Review
Landfills
(https://www.atmos-chem-phys.net/10/1899/2010/acp-10-1899-2010.pdf) In The municipal solid waste landfill as a source of ozone-depleting substances in the United States and United Kingdom by E.L. Hodson, D. Martin, and R. G. Prinn, said researchers look to better understand the relationship between lingering ozonedepleting substance (ODS) emissions and landfills who emit them. It is known fact that ODS have a negative effect on climate change as they weaken the Earth’s defense against the sun: the Ozone. When waste is not disposed of properly in landfills, it has the potential to release ODS. Luckily, the accumulation of ODSs has depleted to the point where tropospheric concentrations are stable or decreasing (1). However, accurate predictions of future ODS emissions is necessary so that appropriate strategies are developed to minimize ozone loss. While there are several different types of ODSs, for the sake of simplicity Hodson et. all decided to study the following four: CFC- 11 (trichlorofluoromethane), CFC-12 (dichlorodifluoromethane), CFC-113 (1,1,2-trichloro-1,2,2-trifluoroethane), and CH3CCl3 (1,1,1-trichloroethane) (1). These four were selected because of their high ozone depleting potential (ODP) and historically were released in large quantities into the atmosphere. Furthermore, they have long lifetimes until they dissolve and this makes these chlorofluorocarbons (CFCs) even more relevant to study. Prior to this study, CFC-11, CFC-12, CFC-1143, and Ch3CCI3 have all been detected at above normal levels in landfills (2). Despite this compelling evidence, there is not any existing national inventory methods to quantify national ODS emissions from landfills (2). In terms of methodology, Hodson et. all studied seven US and nine UK municipal solid waste (MSW) landfills with active gas management (2). It is important to note that these landfills accept more than 50% of their waste from domestic and commercial sources (2). These landfills additionally varied in size- ranging from small, medium and large- to get the most representative sample. Most fields received one-time field sampling; however, one US MSW landfills was sampled every month for over a year (2). The US MSW landfills had triplicate canister samples where they UK MSW landfills only had one canister samples. After performing various chemistry based calculates to get the different percentages of gases that made up the various samples, Hodson et. all developed confidence intervals to best estimate the percentage these CFCs and CH3CCI3 make up of the landfill emissions. The results were the following: CFC-11 (0.037 - 0.074 Gg/y), CFC-12 (0.089 - 0.18 Gg/y), CFC-113 (0.0058 - 0.012 Gg/y), CH3CCl3 (0.012 - 0.024 Gg/y). Understandably, the results without context do not mean much. Luckily, these amounts support the hypothesis that US and MK MSW landfills aren’t a significant source of these gases. In fact, these estimates indicate that US MSW landfills account for less than 1% of total US emissions (10). Thus, this study shows that monitoring and properly disposing of trash can keep ODS levels low, and can make a significant difference.
Water withdrawals
http://pubs.acs.org/doi/full/10.1021/es404830x In Factors Governing Change in Water Withdrawals for U.S. Industrial Sectors from 1997 to 2002, Hui Wang, Mitchell J. Small, and David A. Dzomback looked to understand why water withdrawal levels have been maintained despite a rapidly growing population. Wang et. all define water shortage as the total amount of water withdrawn from its source (1). Water withdrawal can be alleviated through strategies like “water recycling and reuse, stormwater capture, water transfer from more water-rich areas, and desalination of seawater” (1). It was found that in 2002, U.S. sectors like agricultural activities, power generation, and food manufacturing were responsible for 50% of the water withdrawal. As the population grows, it releys more heavily on these basic sectors and can heavily impact water quantity, quality, and availability. Luckily, despite population growth rates of water withdrawal have been increasing at a much slower place. Inspired by this phenomena, Wang et. all decided to study 5 factors: “changes in population, GDP per capita, water use intensity, production structure, and consumption patterns.” In terms of data collection, Wang et. all used the U.S. Bureau of Economic Analysis (BEA) benchmark economic input-output (EIO) table from 1997 and 2002 to gain information about water withdrawals about summary sectors for both years (2). Wang et. all additionally used USGS data to get information about Thermoelectric power generation, Mining, Industrial, Irrigation, Residential, Public supply, Livestock, and Aquaculture. Once the data was collected, Wang et. all created an Economic Input-Output Life Cycle Assessment (EIO-LCA). This uses independent variables that are subsections of the 5 main factors listed above to estimate the overall water withdrawal. From there, a structural decomposition analysis (SDA) was done to see how the variables independent variables influence the the dependent variable as well as their relationship with each other. Upon extensive regression-based modeling, Wang et. all found that water use intensity was able to most accurately predict water withdrawal rates. Thus, it will be important to keep water withdrawal rates in mind when examining state to state.
Power Plants Emissions
https://search-proquest-com.offcampus.lib.washington.edu/docview/1159893328/fulltextPDF/923F8AF99A884B9APQ/1?accountid=14784 “North American Power Plant Air Emissions” describes the Commission for Environmental Cooperation’s (CEC) 2011 report on criteria air pollutant and greenhouse gas emissions from fossil fuel-fired power plants in North America for the year 2005, which was the most recent year for which data was available from United States, Canada, and Mexico, when the report was written. For the purpose of our research we will focus on their analysis of data collected on power plants in the United States. Criteria air pollutants (including sulfur dioxide, and nitrogen oxides) and greenhouse gases are atmospheric pollutants emitted by power plants. It is also possible for power plants to release trace metals, such as mercury, with certain fuels (1). About two thirds of power generation in North America comes from fossil fuels including coal, residual fuel oil, and natural gas, which release air pollutants when burned (2). The CEC’s methodology involved collecting public information on individual fossil fuel-fired power plants, including their installed capacity, electricity generation, technologies used, and fuels burned from public sources including each country’s national emission inventories. They used other public information to calculate estimates where data was absent. Limited availability of data narrowed the CEC’s analysis to data on SO2, NOX, and CO2 emissions (1). For the United States, they obtained data for CO2, NOX, SO2, methane, nitrous oxide and mercury emissions from 2,728 registered facilities. Data on PM-10 (particulate matter with a diameter of 10 mircormeters or less) and PM-2.5 (particulate matter with a diameter of 2.5 micrometers or less) emissions were only included for 1,182 of the facilities (2). In their analysis, the CEC determined there was no significant change in the overall amount of emissions across North America between 2002 and 2005 (3). In 2005, the net electricity generation in the U.S. was 49.7% from coal, 18.7% from natural gas, 5.46% from hydroelectric, 2.3% from renewable energy sources (including biomass, wind, geothermal, and solar), and 3% from other sources (2-3). Electricity generation is the source of about one-third of total greenhouse gas emissions in the U.S, contributing 39.3% of CO2 emissions nation-wide in 2005 (3). The CEC also found that coal-fired power plants were the largest contributor of SO2, NOx, PM-10, PM-2.5, and greenhouse gas emissions in the U.S. (5). ### Summary Now that a sufficient background has been laid down about waste disposal, water withdrawal, and power consumption, this paper will delve into its area of focus. It is clear that extensive research has been in all of these areas individually; however, with a problem as large as climate change, an interdisciplinary approach is required to get a holistic overview of the problem without compromising the quality of information. As a result of this belief, this paper takes on a pragmatic worldview, constructed by a statistician, data-visualization expert, and a data analyst. All of these papers offer great depth, however, this research article will focus on assigning each of the 50 United States a score based on how they are navigating their relationship between waste disposal, power consumption, and water withdrawals.
Method Description
Introduction
Our dataset consists of three major parts that contribute to environmental hazards. We have aggregated and cleaned a few different data sets to fit our needs and the data now lives inside one CSV document. There is quantitative, state by state data on landfills and waste, power consumption and emissions, and water withdrawals and water waste.
Landfills
Source
This data is from the Environmental Protection Agency’s (EPA) Landfill Methane Outreach Program (LMOP). This data is current as of June 2017. The LMOP is an program that aims to reduce methane emissions from landfills. One of the ways they accomplish this goal is through gathering data on landfills and potential energy projects that they have going on at the landfills.
Description
This data has two major parts, information about the landfill and information about methane saving projects at the landfill. The landfill data is a collection of 2,400 municipal solid waste landfills across the United States. This data has been collected largely in part due to the partnerships the EPA has with landfills. It includes landfills that both do and don’t report to the EPA’s Greenhouse Gas Reporting Program. The landfill data includes information like the state it’s located in, the landfill’s name, the landfill operator’s name (or organization), the county it’s located in, the latitude and longitude of the plant and the amount of trash it contains (in tons); to name a few. It also contains information about landfill operations including the year it opened, the year it has closed if it has closed, if they have have methane protection projects going on, the year those methane protection projects started, and much more.
Power Plant Emissions
Source
This data has also been collected by the EPA and is current as of 2014. It is a part of the EPA’s Emissions & Generated Resource Integrated Database (eGRID). The EPA describes eGRID as “a comprehensive source of data on the environmental characteristics of almost all electric power generated in the United States” (EPA). They collect data on air emissions for nitrogen oxides, sulfur dioxide, carbon dioxide, methane, and nitrous oxide; emissions rates; net generation; resource mix; and many other attributes.
There are three major sources that the eGRID gets it’s data from: EPA/CAMD: this includes data reported to EPA by electric generating units to comply with the regulations in 40 CFR Part 75. Data include annual emissions of CO2, NOx, and SO2; ozone season emissions of NOx; and annual and ozone season generation and heat input. The data is available at https://www.epa.gov/airmarkets. EIA-860: this includes data reported to EIA on electric generators. Data include nameplate capacity, prime mover, primary fuel type, and indication of whether the generator is a combined-heat-and-power unit (EIA, 2016a). EIA-923: this includes data reported to EIA on fuel consumption and generation. Data include monthly generation and heat input at the unit or generator level for a subset of units and generators, and at the prime mover level for all plants. eGRID2014 uses unit- or generator-level data where available, and prime mover-level data for all other units and generators (EIA, 2016b).
Description
This data set comes with hundreds of columns, we have narrowed down those to just a few that are important to our mission. It describes the different amounts of emission of the different greenhouse gases, the amount of energy it produces and the type of energy it produces. It also shows summary statistics on the amount of renewable vs nonrenewable energy that it generates and combustible v.s. noncombustible energy that it generates.
Water Withdrawals
Source
The data was gathered from the United States Geological Survey. This agency collects, monitors and assess data on different aspects of the United States’ geology for both policy makers and public interest. Their data is about many different aspects including land, air, water and everything in between. Our data comes from the USGS Water Science School’s report on Water Usage and is current as of 2010.
Description
This data describes how much water is being used by the population of the United States. The data has been aggregated to reflect a state by state total for each column. The data is separated into many different parts, including where the water is coming from (surface water or groundwater), where it’s going to (agriculture, mining, industrial, livestock, aquaculture), who is using it (publicly supplied, domestically supplied), and the type of water (fresh or saline). There is also information on the populations for each state and the populations that use this water.
Analysis
<<<<<<< HEADWe will be performing multiple regression to see if there is a linear, predictive relationship between the rankings we assign to the states (dependent variable) and the scores that compose said ranking (independent variables) which are built on the following 6 categories: total emissions, total water withdrawals, total waste, percentage renewable, total LFG collected, and percentage non-combustible. This test was done to make sure how we weighted and created the scores aligns well with the overall ranking system. There are 3 checks to make sure multiple regression is the appropriate test to conduct:
1. There is a linear relationship between the independent and dependent variables.
Unfortunately, there does not seem to be a strong linear relationship in the scatterplots above. However, we are not seeing any different types of patterns such as logarithmic or exponential functions which are even more worrisome. As a result, we will proceed cautiously.
## NULL## NULL## NULL## NULL## NULL## NULL
2. For each value of X, the probability distribution of Y has the same standard deviation and the y values are independent.
The easiest way to evaluate this check is to check the graphs of the residuals given the fitted value. The variability of the residuals should fairly consistent given any value of x and there shouldn’t be a clear pattern.
## numeric(0)As displayed by the graph, it seems to be an okay fit! There isn’t a clear pattern to the residuals and they appear to vary by approximately the same amount.
3. The dependent variable’s distribution is roughly normally distributed
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## $xname
## [1] "df$scores"
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## [1] TRUE
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## attr(,"class")
## [1] "histogram"As one can see, there is a uniform distribution for scores. This is not a normal distribution; however, there is a lack of skew or signs of being bimodal and thus we will proceed with caution.
Now that the checks have been discussed, we can proceed and go over the results in the data analysis section.
We will be performing multiple regression to see if there is a linear, predictive relationship between the rankings we assign to the states (dependent variable) and the scores that compose said ranking (independent variables) which are built on the following 6 categories: total emissions, total water withdrawals, total waste, percentage renewable, total LFG collected, and percentage non-combustible. This test was done to make sure how we weighted and created the scores aligns well with the overall ranking system. There are 3 checks to make sure multiple regression is the appropriate test to conduct:
- There is a linear relationship between the independent and dependent variables.
- For each value of X, the probability distribution of Y has the same standard deviation and the y values are independent.
- The dependent variable’s distribution is roughly normally distributed.
Data Analysis
Introduction
Our analysis revolves around six variables that we chose to represnent enviromental impacts of the different states. The six variables were chosen out of our three major datasets (Water Withdrawals, Landfills, and Power Plants). First we looked at the total amount of emissions in tons per capita per state. Second we took the total water withdrawals in million of gallons per day as a percentage of the population in each state. Third was the amount waste in tons per person for each state. Fourth was the percentage of renewable energy vs the total energy produced by each state. The fifth variable was amount of emissions collected through an LFG system for each state. The last variable we took into consideration was the rate of combustable fuel vs non combustable fuels for each state.
Based on our research about our data and the environemnt in general we were able to conclude that these 6 variables had a significant impact on our environment. We knew that these variables didn’t have the same amount of impact so the next step was to determine which variables impacted the environment more or less. We consoluted an expert in the field as well as our own research in order to determine how we would weigh each variable. Our research concluded that they should be placed in the following order:
- Total emissions
- Total water withdrawals
- Total waste
- Percentage of renewable energy
- Total LFG collected
- Percentage of non-combustibles
Total emissions, water with drawals and waste were all ranked at the top because they were the most direct impacts on the environemnt. Grennhouse gas emissions have been proven to directly cause climate change on Earth, lending this to be the first in our rankings of variables. Water withdrawals also directly contribute to the earth’s climate problem because it is measuring the imapct humans are having on the water ecosystems. Waste comes in third because it is both influencing ecosystems by poisining land and contributing to the Greenhouse gas emissions. Renewable energy and LFG collection are important to the prevention of greenhouse gas emissions. And finally, the percentage of non-combustible gasses which have potential to be dangerous to our earth, but aren’t directly influencing climate change.
Once we ranked the variables we decided on a weighted system to determine each state’s score. We weighted the more important variables heavier than the less important (based on our rankings). The total score comprised of:
25% : Total emissions
20% : Water Withdrawals
16% : Total waste
14% : Percentage of renewable energy
13% : Total LFG collected
12% : Percentage of non-combustibles
These factors contributed to our total score for each state. The rest of our analysis was completed based on this score.
Summary Statistics
| variable | mean | median |
|---|---|---|
| total emissions by net generation | 0.5489785 | 0.5406953 |
| total water withdrawals by population | 1.6952444 | 1.0211021 |
| total waste by population | 34.8130241 | 30.1075724 |
| renewables ratio | 0.1749627 | 0.0894551 |
| lfg collected by population | 0.0000125 | 0.0000072 |
| noncombustion ratio | 0.3268161 | 0.3385155 |
Multivariate Regression
<<<<<<< HEADThe following equation was created by the linear regression function in R:
Predicted Rank = -15.6131 + 28.944 (emissions) – 0.9015 (withdrawals) + 50.2727 (waste) – 10.3764 (combustion) + 34.6818 (lfg.collected) – 21.3631 (renews)
By creating a linear regression function in R, R additionally runs an F-test. The null hypothesis for an f-test is that a model with only a y-intercept is just as good a fit as the model created through linear regression. After conducting the f-test, we received a sample statistic of 4.772. If we were to conduct a million f-tests with a million different randomly selected samples, the odds of getting a more extreme f-statistic is 0.08%. As a result, we can reject the null hypothesis. This means that we have created a model that is better than a model with only the y-intercept.
Overall, this confirms that we have made a model for assigning ranks that is fairly predictable which is encouraging. This means that if one only knew information about the 6 independent variables, they could use this equation to get a predicted rank that would be very close to correct.
The following equation was created by the linear regression function in R:
Predicted Rank = -15.6131 + 28.944 (emissions) – 0.9015 (withdrawals) + 50.2727 (waste) – 10.3764 (combustion) + 34.6818 (lfg.collected) – 21.3631 (renews)
By creating a linear regression function in R, R additionally runs an F-test. The null hypothesis for an f-test is that a model with only a y-intercept is just as good a fit as the model created through linear regression. After conducting the f-test, we received a sample statistic of 4.772. If we were to conduct a million f-tests with a million different randomly selected samples, the odds of getting a more extreme f-statistic is 0.08%. As a result, we can reject the null hypothesis. This means that we have created a model that is better than a model with only the y-intercept.
Overall, this confirms that we have made a model for assigning ranks that is fairly predictable which is encouraging. This means that if one only knew information about the 6 independent variables, they could use this equation to get a predicted rank that would be very close to correct.
Results of the checks
The results of the checks to make sure multiple regression is an appropriate test as as follows:
1. There is a linear relationship between the independent and dependent variables.
Unfortunately, there does not seem to be a strong linear relationship in the scatterplots above. However, we are not seeing any different types of patterns such as logarithmic or exponential functions which are even more worrisome. As a result, we will proceed cautiously.
2. For each value of X, the probability distribution of Y has the same standard deviation and the y values are independent.
The easiest way to evaluate this check is to check the graphs of the residuals given the fitted value. The variability of the residuals should fairly consistent given any value of x and there shouldn’t be a clear pattern.
3. The dependent variable’s distribution is roughly normally distributed
As one can see, there is a uniform distribution for scores. This is not a normal distribution; however, there is a lack of skew or signs of being bimodal and thus we will proceed with caution. >>>>>>> d3bdd213aace64cd0a381af2376d971ff5e7ef51
Disussion
Overall, we synthesized information from three unique datasets that had one common binding: the 50 United States. We picked out the six most important traits across all three of these data frames, and ranked as well as weighted them based on importance. As discussed above, the word importance is inherently subjective. If one were to choose different weights for these six traits, one would receive a completely different rank for the 50 states. Thus, it is here we will check our assumptions. We assigned these weights based off of feedback from Lynn McGuire, a subject matter expert in environmental conservation, and her experience in the field. By incorporating her feedback as well as key takeaways from the literature review, we hope to mitigate our bias.
In addition to receiving feedback from subject matter experts, we also ran a multivariate regression test to make sure one could predict rank based off of these scores we assigned to these 6 factors by states. The model proved to be statistically significant and assure us that our math checked out. That being said, all models are inherently wrong; they just give us an idea of approximation. To clarify, this multivariate regression analysis gave us assurance that our math was correct, but does not affirm our ranking system itself is inherently correct.
As for the rankings, the overall order did not surprise us. ALI TAKE IT AWAY (AKA INTERPRET THE RANKINGS)
Appendix
Column Tables
Landfill
| Column Name | Description |
|---|---|
| Landfill Name | Name of the landfill |
| State | State that the landfill is in |
| Physical Address | Address of the landfill |
| City | City landfill is located in or near |
| County | County landfill is located in or near |
| Zip Code | Zip code for the landfill |
| Latitude | Latitude coordinate (decimal) for the landfill |
| Longitude | Longitude coordinate (decimal) for the landfill |
| Ownership Type | “Indicates if landfill is publicly owned, privately owned, or co-owned by public and private entities” |
| Landfill Owner Organization(s) | Organization that owns the landfill |
| Year Landfill Opened | Year landfill opened or began accepting waste (YYYY) |
| Landfill Closure Year | Year landfill stopped accepting waste or is expected to stop accepting waste ÿor year landfill closed or is expected to close (YYYY) |
| Current Landfill Status | Open/Closed status of landfill |
| Waste in Place (tons) | Waste-in-place at the landfill in short tons |
| Waste in Place Year | Year corresponding to the waste-in-place at the landfill (YYYY) |
| LFG Collection System In Place? | “Is there a landfill gas collection system in place? ‘Yes’ for an active GCCS; ‘No’ if landfill has no gas collection system, is passively venting/flaring, or has perimeter gas wells.” |
| LFG Collected (mmscfd) | Amount of landfill gas being collected in million standard cubic feet per day |
| LFG Flared (mmscfd) | “Amount of landfill gas flared (if project is operational, amount of landfill gas flared in back-up flare(s)) in million standard cubic feet per day” |
| Current Project Status | “Current project status (Operational, Construction, Planned, Shutdown, Candidate, Potential, Other)” |
| Project Start Date | Date project became operational (MM/DD/YYYY) |
| Project Shutdown Date | Date project shut down (MM/DD/YYYY) |
| LFG Energy Project Type | Specific type of LFG energy project technology |
| Project Type Category | Specific type of LFG energy project technology |
| MW Capacity | Capacity in megawatts for electricity-generating projects |
| Current Year Emission Reductions (MMTCO2e/yr) - Direct | Direct methane reductions by the energy project for the current year |
| Current Year Emission Reductions (MMTCO2e/yr) - Avoided | Avoided carbon dioxide emission reductions by the energy project for the current year |
Power
| Column Name | Column Name ID | Description |
|---|---|---|
| State abbreviation | PSTATABB | The state_s name abbreviation |
| State nameplate capacity (MW) | NAMEPCAP | It is the intended full-load sustained output of a facility |
| State total annual heat input (MMBtu) | STHTIANT | “The total annual heat input from combustion and noncombustion units, in MMBtu, for the plant. For CHP plants, the value is adjusted by the electric allocation factor.” |
| State annual net generation (MWh) | STNGENAN | The state_s total reported net generation in MWh. |
| State ozone season net generation (MWh) | STNGENOZ | “The state_s total five-month ozone season (May through September) net generation in MWh, based on monthly generator generation data.” |
| State annual NOx emissions (tons) | STNOXAN | “The total annual NOx emissions, in short tons, for the state. Biogas components are adjusted. For CHP plants, the value is adjusted by the electric allocation factor. This adjusted emissions field is estimated by first making the biogas adjustment (if it exists) and then applying the electric allocation factor (if the plant is a CHP).” |
| State ozone season NOx emissions (tons) | STNOXOZ | “The five-month ozone season (May through September) NOx emissions, in short tons, for the state. Biogas components are adjusted. For CHP plants, the value is adjusted by the electric allocation factor. This adjusted emissions field is estimated by first making the biogas adjustment (if it exists) and then applying the electric allocation factor (if the plant is a CHP).” |
| State annual SO2 emissions (tons) | STSO2AN | “The total annual SO2 emissions, in short tons, for the state. Landfill gas components are adjusted. For CHP plants, the value is adjusted by the electric allocation factor. This adjusted emissions field is estimated by first making the landfill gas adjustment (if it exists) and then applying the electric allocation factor (if the plant is a CHP).” |
| State annual CO2 emissions (tons) | STCO2AN | “The total annual CO2 emissions, in short tons, for the state. All CO2 emissions from biomass fuels are adjusted to zero. For CHP plants, the value is adjusted by the electric allocation factor. This adjusted emissions field is estimated by first making the biomass adjustment (if it exists) and then applying the electric allocation factor (if the plant is a CHP).” |
| State annual CH4 emissions (lbs) | STCH4AN | “The total annual CH4 emissions, in pounds, for the state. Biogas biomass components are adjusted. For CHP plants, the value is adjusted by the electric allocation factor. This adjusted emissions field is estimated by first making the biomass adjustment (if it exists) and then applying the electric allocation factor (if the plant is a CHP).” |
| State annual N2O emissions (lbs) | STN2OAN | “The total annual N2O emissions, in pounds for the state. Biogas biomass components are adjusted. For CHP plants, the value is adjusted by the electric allocation factor. This adjusted emissions field is estimated by first making the biomass adjustment (if it exists) and then applying the electric allocation factor (if the plant is a CHP).” |
| State annual coal net generation (MWh) | STGENACL | “The plant annual net generation, in MWh, for coal. Fuel codes that are included in coal are BIT, COG, SUB, LIG, WC, and SC.” |
| State annual oil net generation (MWh) | STGENAOL | “The plant annual net generation, in MWh, for oil. Fuel codes included in oil are DFO, JF, KER, OO, PC, RFO, RG, and WO.” |
| State annual gas net generation (MWh) | STGENAGS | “The plant annual net generation, in MWh, for natural gas. Fuel codes included in gas are NG and PG” |
| State annual nuclear net generation (MWh) | STGENANC | “The plant annual net generation, in MWh, for nuclear. Fuel codes include NUC.” |
| State annual hydro net generation (MWh) | STGENAHY | “The plant annual net generation, in MWh, for hydro. Fuel codes include WAT.” |
| State annual biomass net generation (MWh) | STGENABM | “The annual net generation, in MWh, for biomass. Biomass is a fuel derived from organic matter such as wood and paper products, agricultural waste, or methane (e.g., from landfills). The renewable portion of solid waste, fuel code MSB, is included as biomass, as are AB, BLQ, DG, LFG, ME, OBL, OBS, PP, SLW, WDL, and WDS.” |
| State annual wind net generation (MWh) | STGENAWI | “The plant annual net generation, in MWh, for wind. Fuel codes include WND.” |
| State annual solar net generation (MWh) | STGENASO | “The plant annual net generation, in MWh, for solar. Fuel codes include SUN.” |
| State annual geothermal net generation (MWh) | STGENAGT | “The plant annual net generation, in MWh, for geothermal. Fuel codes include GEO.” |
| State annual other fossil net generation (MWh) | STGENAOF | “The plant annual net generation, in MWh, for other fossil fuel that cannot be categorized as coal, oil, or gas. Other fossil fuel codes include BFG, COG, HY, LB, MH, MSF, OG, PRG, and TDF.” |
| State annual other unknown/ purchased fuel net generation (MWh) | STGENAOP | “The plant annual net generation, in MWh, for other unknown/purchased. Fuel codes include OTH, PUR, or WH.” |
| State annual total nonrenewables net generation (MWh) | STGENATN | “The annual total nonrenewables net generation, in MWh, for the plant. Nonrenewables are exhaustible energy resources such as coal, oil, gas, other fossil, nuclear power, and other unknown/purchased fuel. This field is the sum of STGENACL, STGENAOL, STGENAGS, STGENAOF, STGENANC, and STGENAOP.” |
| State annual total renewables net generation (MWh) | STGENATR | “The annual total renewables net generation, in MWh, for the plant. Renewables are inexhaustible energy resources such as biomass, wind, solar, geothermal, and hydro. This field is the sum of STGENABM, STGENAWI, STGENASO, STGENAGT, and STGENAHY.” |
| State annual total nonhydro renewables net generation (MWh) | STGENATH | “The annual total nonhydro renewables net generation, in MWh, for the plant. This field is the sum of STGENABM, STGENAWI, STGENASO, and STGENAGT.” |
| State annual total combustion net generation (MWh) | STGENACY | “The annual total combustion net generation, in MWh, for the plant. This field is the sum of STGENACL, STGENAOL, STGENAGS, STGENAOF, STGENABM, and STGENAOP.” |
| State annual total noncombustion net generation (MWh) | STGENACN | “The annual total noncombustion net generation, in MWh, for the plant. This field is the sum of STGENANC, STGENAHY, STGENAWI, STGENASO, and STGENAGT.” |
Water
| Column Name | Description | |
|---|---|---|
| State | The state_s name | |
| Population2010 | The population in 2010 | |
| Total-Fresh | “Total withdrawals, fresh, in Mgal/d” | |
| Total-Saline | “Total withdrawals, saline, in Mgal/d” | |
| Total | “Total withdrawals, total (fresh+saline), in Mgal/d” | |
| Groundwater-Fresh | “Total groundwater withdrawals, fresh, in Mgal/d” | |
| Groundwater-Saline | “Total groundwater withdrawals, saline, in Mgal/d” | |
| Groundwater-Total | “Total groundwater withdrawals, total (fresh+saline), in Mgal/d” | |
| Surfacewater-Fresh | “Total surface-water withdrawals, fresh, in Mgal/d” | |
| Surfacewater-Saline | “Total surface-water withdrawals, saline, in Mgal/d” | |
| Surfacewater-Total | “Total surface-water withdrawals, total (fresh+saline), in Mgal/d” | |
| PublicSuply | “Public Supply, total population served, in thousands” | |
| Self-SuppliedDomestic | “Domestic, self-supplied population, in thousands” | |
| Irrigation | “Irrigation, total withdrawals, fresh, in Mgal/d” | |
| LiveStock | “Livestock, total withdrawals, fresh, in Mgal/d” | |
| Aquaculture | “Aquaculture, total withdrawals, total (fresh+saline), in Mgal/d” | |
| SelfSuppliedIndustrial-Saline | “Industrial, self-supplied total withdrawals, saline, in Mgal/d” | |
| SelfSuppliedIndustrial-Fresh | “Industrial, self-supplied total withdrawals, fresh, in Mgal/d” | |
| Mining-Fresh | “Mining, total withdrawals, fresh, in Mgal/d” | |
| Mining-Saline | “Mining, total withdrawals, saline, in Mgal/d” | |
| ThermoelectricPower-Saline | “Thermoelectric, total withdrawals, saline, in Mgal/d” | |
| ThermoelectricPower-Fresh | “Thermoelectric, total withdrawals, fresh, in Mgal/d” | |
| Population-Total | “Total population of county, in thousands” | |
| Population-Served | “Public Supply, total population served, in thousands” | |
| Population-Served-Percent | Population served over the total population | |
| PublicWithdrawals-Groundwater | “Public Supply, groundwater withdrawals, total, in Mgal/d” | |
| PublicWithdrawals-Surfacewater | “Public Supply, surface-water withdrawals, total, in Mgal/d” | |
| PublicWithdrawls-Total | “Public Supply, total withdrawals, total (fresh+saline), in Mgal/d” | |
| WaterDomesticUse | “Domestic, total use (withdrawals + deliveries)” | |
| DomesticUse-Percent | Domestic use over the total population | |
| AllOtherUse | Other uses other than domestic or public supplied | |
| Irrigation-Groundwater | “Irrigation, groundwater withdrawals, fresh, in Mgal/d” | |
| Irigation-Surfacewater | “Irrigation, surface-water withdrawals, fresh, in Mgal/d” | |
| Irigation-Total | “Irrigation, total withdrawals, fresh, in Mgal/d” | |
| Livestock-Groundwater | “Livestock, groundwater withdrawals, fresh, in Mgal/d” | |
| Livestock-Surfacewater | “Livestock, surface-water withdrawals, fresh, in Mgal/d” | |
| Livestock-Total | “Livestock, total withdrawals, fresh, in Mgal/d” | |
| Aquaculture-Groundwater | “Aquaculture, groundwater withdrawals, total, in Mgal/d” | |
| Aquaculture-Surfacewater | “Aquaculture, surface-water withdrawals, total, in Mgal/d” | |
| Aquaculture-Total | “Aquaculture, total withdrawals, total (fresh+saline), in Mgal/d” | |
| Mining-Groundwater | “Mining, groundwater withdrawals, total, in Mgal/d” | |
| Mining-Surfacewater | “Mining, surface-water withdrawals, total, in Mgal/d” | |
| Mining-Total | “Mining, total withdrawals, total (fresh+saline), in Mgal/d” | |
| Thermoelectric-Groundwater | “Thermoelectric, groundwater withdrawals, total, in Mgal/d” | |
| Thermoelectric-Surfacewater | “Thermoelectric, surface-water withdrawals, total, in Mgal/d” | |
| Thermoelectric-Total | “Thermoelectric, total withdrawals, total (fresh+saline), in Mgal/d” | |
| ThermoelectricPower-Generated | “Thermoelectric, power generated, in gigawatt-hours” |